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Computer aided detection of mercury heavy metal intoxicated fish: an application of machine vision and artificial intelligence technique

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Abstract

Heavy metal pollution in our aquatic bodies is a major health concern in the present scenario. The harmful effect of non-biodegradable toxic and trace metals is more serious than other contaminants. Fishes are more susceptible to various harmful impacts of these pollutants within the aquatic environment. Heavy metal toxicity from fish intake can cause health problems such as multi-organ damage, and serious diseases. Due to bioaccumulation through the food chain and direct absorption of these heavy metals, it is very important to monitor the quality of food fishes. Classical chemical-based methods for the assessment of fish quality are destructive and at the same time, they also require costly machines and expert manpower. In the present work, a machine learning-based methodology has been employed in which the suitable color and texture features have been identified and have been genetically optimised for the classification of heavy metal exposed and non-exposed fish using a machine learning classifier. The performance of the proposed method has also been tested using transfer learning-based approach. The best F1-score of 97.1% and 93.5% have been obtained in the case of the proposed genetically optimised color texture features-based approach and the transfer learning-based approach respectively. Thus, the proposed technique can be utilised to identify heavy metal-contaminated fish and to mitigate possible consequences. The proposed method can also be used for large-scale fish processing.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Adebayo J, Gilmer J, Muelly M et al (2018) Sanity checks for saliency maps. Advances in Neural Information Processing Systems, In

  2. Amakdouf H, Zouhri A, El Mallahi M et al (2021) Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments. Multimed Tools Appl 80:3173–3192. https://doi.org/10.1007/s11042-020-09781-x

    Article  Google Scholar 

  3. Authman MMN, Abbas HH, Abbas WT (2013) Assessment of metal status in drainage canal water and their bioaccumulation in Oreochromis niloticus fish in relation to human health. Environ Monit Assess 185:891–907. https://doi.org/10.1007/s10661-012-2599-8

    Article  Google Scholar 

  4. Banwari A, Joshi RC, Sengar N, Dutta MK (2022) Computer vision technique for freshness estimation from segmented eye of fish image. Ecol Inform 69:101602

    Article  Google Scholar 

  5. Bose MTJ, Ilavazhahan M, TamilselvI R, Viswanathan M (2013) Effect of heavy metals on the histopathology of gills and brain of fresh water fish Catla catla. Biomed Pharmacol J 6:99–105. https://doi.org/10.13005/bpj/390

    Article  Google Scholar 

  6. Boyd CE (2005) LC 50 calculations help predict toxicity. Glob Aquac Advocate, p 84,87

    Google Scholar 

  7. Chandra Joshi R, Mishra R, Gandhi P, … Dutta MK (2021) Ensemble based machine learning approach for prediction of glioma and multi-grade classification. Comput Biol Med 137:104829. https://doi.org/10.1016/j.compbiomed.2021.104829

    Article  Google Scholar 

  8. Cheng JH, Dai Q, Sun DW, … Pu HB (2013) Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends Food Sci Technol 34:18–31

    Article  Google Scholar 

  9. Ching T, Himmelstein DS, Beaulieu-Jones BK, … Greene CS (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15:20170387. https://doi.org/10.1098/rsif.2017.0387

    Article  Google Scholar 

  10. de Souza Hacon S, Oliveira-da-Costa M, de Souza Gama C et al (2020) Mercury exposure through fish consumption in traditional communities in the Brazilian northern Amazon. Int J Environ Res Public Health 17:5269. https://doi.org/10.3390/ijerph17155269

    Article  Google Scholar 

  11. de Souza Hacon S, Oliveira-da-Costa M, de Souza Gama C et al (2020) Mercury exposure through fish consumption in traditional communities in the Brazilian northern Amazon. Int J Environ Res Public Health 17:5269. https://doi.org/10.3390/ijerph17155269

    Article  Google Scholar 

  12. de Vasconcellos ACS, Hallwass G, Bezerra JG et al (2021) Health risk assessment of mercury exposure from fish consumption in Munduruku indigenous communities in the Brazilian Amazon. Int J Environ Res Public Health 18:7940. https://doi.org/10.3390/ijerph18157940

    Article  Google Scholar 

  13. Dhanakumar S, Solaraj G, Mohanraj R (2015) Heavy metal partitioning in sediments and bioaccumulation in commercial fish species of three major reservoirs of river Cauvery delta region, India. Ecotoxicol Environ Saf 113:145–151. https://doi.org/10.1016/j.ecoenv.2014.11.032

    Article  Google Scholar 

  14. Djedjibegovic J, Marjanovic A, Tahirovic D, … Caklovica F (2020) Heavy metals in commercial fish and seafood products and risk assessment in adult population in Bosnia and Herzegovina. Sci Rep 10:13238. https://doi.org/10.1038/s41598-020-70205-9

    Article  Google Scholar 

  15. Dowlati M, de la Guardia M, Dowlati M, Mohtasebi SS (2012) Application of machine-vision techniques to fish-quality assessment. TrAC - Trends Anal, Chem

    Book  Google Scholar 

  16. Dowlati M, Mohtasebi SS, Omid M, … de la Guardia M (2013) Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J Food Eng 119:277–287. https://doi.org/10.1016/j.jfoodeng.2013.05.023

    Article  Google Scholar 

  17. Dutta MK, Issac A, Minhas N, Sarkar B (2016) Image processing based method to assess fish quality and freshness. J Food Eng 177:50–58. https://doi.org/10.1016/j.jfoodeng.2015.12.018

    Article  Google Scholar 

  18. Ekoputris RO (2018) MobileNet: Deteksi Objek pada Platform Mobile. 9 May 2018

  19. Fazio F, Piccione G, Tribulato K, … Faggio C (2014) Bioaccumulation of heavy metals in blood and tissue of striped mullet in two Italian Lakes. J Aquat Anim Health 26:278–284. https://doi.org/10.1080/08997659.2014.938872

    Article  Google Scholar 

  20. Fu Z, Xi S (2020) The effects of heavy metals on human metabolism. Toxicol Mech Methods 30(3):167–176. https://doi.org/10.1080/15376516.2019.1701594

    Article  Google Scholar 

  21. Georgieva E, Velcheva I, Yancheva V, Stoyanova S (2014) Trace metal effects on gill epithelium of common carp Cyprinus carpio L. (cyprinidae) Acta Zool. Bulgarica. 66:277–282

    Google Scholar 

  22. Hashim R, Song TH, Muslim NZM, Yen TP (2014) Determination of heavy metal levels in fishes from the lower reach of the Kelantan River, Kelantan, Malaysia. Trop life Sci Res 25:21–39

    Google Scholar 

  23. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics).

  24. Huseen HM, Mohammed AJ (2019) Heavy metals causing toxicity in fishes. J Phys Conf Ser 1294:62028. https://doi.org/10.1088/1742-6596/1294/6/062028

    Article  Google Scholar 

  25. Isangedighi IA, David GS (2019) Heavy metals contamination in fish: effects on human health. J Aquatic Sci Marine Biol 2:7–12

    Google Scholar 

  26. Ishaq O et al (2017) Deep fish. SLAS Discovery: Advancing Life Sciences R & D 22(1):102–107. https://doi.org/10.1177/1087057116667894

    Article  Google Scholar 

  27. Issac A, Srivastava A, Srivastava A, Dutta MK (2019) An automated computer vision based preliminary study for the identification of a heavy metal (hg) exposed fish-channa punctatus. Comput Biol Med 111:103326. https://doi.org/10.1016/j.compbiomed.2019.103326

    Article  Google Scholar 

  28. Järup L (2003) Hazards of heavy metal contamination. Br Med Bull 68:167–182. https://doi.org/10.1093/bmb/ldg032

    Article  Google Scholar 

  29. Javed M, Usmani N (2019) An overview of the adverse effects of heavy metal contamination on fish health. Proc Natl Acad Sci India Sect B Biol Sci 89:389–403. https://doi.org/10.1007/s40011-017-0875-7

    Article  Google Scholar 

  30. Joshi RC, Joshi M, Singh AG, Mathur S (2018) Object detection, classification and tracking methods for video surveillance: a review. In: 2018 4th international conference on computing communication and automation, ICCCA 2018.

  31. Kawser Ahmed M, Baki MA, Kundu GK, … Muzammel Hossain M (2016) Human health risks from heavy metals in fish of Buriganga river, Bangladesh. Springerplus 5:1697. https://doi.org/10.1186/s40064-016-3357-0

    Article  Google Scholar 

  32. Lim JW, Kim TY, Woo MA (2021) Trends in sensor development toward next-generation point-of-care testing for mercury. Biosens Bioelectron 183:113228

    Article  Google Scholar 

  33. Ling H, Wu J, Huang J, … Li P (2020) Attention-based convolutional neural network for deep face recognition. Multimed Tools Appl 79:5595–5616. https://doi.org/10.1007/s11042-019-08422-2

    Article  Google Scholar 

  34. Litjens G, Kooi T, Bejnordi BE, … Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  35. Maier D, Blaha L, Giesy JP, … Triebskorn R (2015) Biological plausibility as a tool to associate analytical data for micropollutants and effect potentials in wastewater, surface water, and sediments with effects in fishes. Water Res 72:127–144. https://doi.org/10.1016/j.watres.2014.08.050

    Article  Google Scholar 

  36. Mathivanan R (2004) Effects of sublethal concentration of quinophos on selected respiratory and biochemical parameters in the fresh water fish Oreochromis mossambicus. J Ecotoxicol Environ Monit 14(1):57–64

    Google Scholar 

  37. Maurya PK, Malik DS, Yadav KK, … Kamyab H (2019) Bioaccumulation and potential sources of heavy metal contamination in fish species in river ganga basin: possible human health risks evaluation. Toxicol Reports 6:472–481. https://doi.org/10.1016/j.toxrep.2019.05.012

    Article  Google Scholar 

  38. Mehmood MA, Qadri H, Bhat RA, … Shafiq-ur-Rehman (2019) Heavy metal contamination in two commercial fish species of a trans-Himalayan freshwater ecosystem. Environ Monit Assess 191:104. https://doi.org/10.1007/s10661-019-7245-2

    Article  Google Scholar 

  39. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform

  40. Oliveira Ribeiro CA, Vollaire Y, Sanchez-Chardi A, Roche H (2005) Bioaccumulation and the effects of organochlorine pesticides, PAH and heavy metals in the eel (Anguilla anguilla) at the Camargue nature reserve. France Aquat Toxicol 74:53–69. https://doi.org/10.1016/j.aquatox.2005.04.008

    Article  Google Scholar 

  41. Poleksic V, Lenhardt M, Jaric I, … Raskovic B (2010) Liver, gills, and skin histopathology and heavy metal content of the Danube sterlet (Acipenser ruthenus Linnaeus, 1758). Environ Toxicol Chem 29:515–521. https://doi.org/10.1002/etc.82

    Article  Google Scholar 

  42. Rehman K, Fatima F, Waheed I, Akash MSH (2018) Prevalence of exposure of heavy metals and their impact on health consequences. J Cell Biochem 119(1):157–184. https://doi.org/10.1002/jcb.26234

    Article  Google Scholar 

  43. Roméo M, Siau Y, Sidoumou Z, Gnassia-Barelli M (1999) Heavy metal distribution in different fish species from the Mauritania coast. Sci Total Environ 232:169–175. https://doi.org/10.1016/S0048-9697(99)00099-6

    Article  Google Scholar 

  44. Romero-Romero S, García-Ordiales E, Roqueñí N, Acuña JL (2022) Increase in mercury and methylmercury levels with depth in a fish assemblage. Chemosphere 292:133445. https://doi.org/10.1016/j.chemosphere.2021.133445

    Article  Google Scholar 

  45. Santhakumar M, Balaji M (2000) Acute toxicity of an organophosphorus insecticide monocrotophos and its effects on behaviour of an air-breething fish, Anabas testudineus (Bloch). J Environ Biol 21(2):121–123

    Google Scholar 

  46. Selvaraju RR, Cogswell M, Das A et al (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128:336–359. https://doi.org/10.1007/s11263-019-01228-7

    Article  Google Scholar 

  47. Sengar N, Dutta MK, Sarkar B (2017) Computer vision based technique for identification of fish quality after pesticide exposure. Int J Food Prop:1–15. https://doi.org/10.1080/10942912.2017.1368553

  48. Sengar N, Gupta V, Dutta MK, Travieso CM (2018) Image processing based method for identification of fish freshness using skin tissue. In: 2018 4th international conference on Computational Intelligence & Communication Technology (CICT), pp 1–4

    Google Scholar 

  49. Sfakianakis DG, Renieri E, Kentouri M, Tsatsakis AM (2015) Effect of heavy metals on fish larvae deformities: a review. Environ Res 137:246–255. https://doi.org/10.1016/j.envres.2014.12.014

    Article  Google Scholar 

  50. Sharma K, Sharma P, Dhiman SK, … Saini HS (2022) Biochemical, genotoxic, histological and ultrastructural effects on liver and gills of fresh water fish Channa punctatus exposed to textile industry intermediate 2 ABS. Chemosphere 287:132103. https://doi.org/10.1016/j.chemosphere.2021.132103

    Article  Google Scholar 

  51. Shen J, Robertson N (2021) BBAS: towards large scale effective ensemble adversarial attacks against deep neural network learning. Inf Sci (Ny) 569:469–478. https://doi.org/10.1016/j.ins.2020.11.026

    Article  Google Scholar 

  52. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR 2015 - conference track proceedings.

  53. Singh A, Gupta H, Srivastava A, … Dutta MK (2021) A novel pilot study on imaging-based identification of fish exposed to heavy metal (hg) contamination. J Food Process Preserv 45. https://doi.org/10.1111/jfpp.15571

  54. Swain KK, Balasubramaniam R, Bhand S (2020) A portable microfluidic device-based Fe3O4–urease nanoprobe-enhanced colorimetric sensor for the detection of heavy metals in fish tissue. Prep Biochem Biotechnol 50:1000–1013. https://doi.org/10.1080/10826068.2020.1780611

    Article  Google Scholar 

  55. Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

  56. Taweel AKA, Shuhaimi-Othman M, Ahmad AK (2012) Analysis of heavy metal concentrations in tilapia fish (Oreochromis niloticus) from four selected markets in Selangor. Peninsular Malaysia J Biol Sci. https://doi.org/10.3923/jbs.2012.138.145

  57. Vasanthi N, Muthukumaravel K, Sathick O, Sugumaran J (2019) Toxic effect of mercury on the freshwater fish oreochromis mossambicus. Res J life Sci Bioinform Pharm Chem Sci 5(3):365–376Page 23/31. https://doi.org/10.26479/2019.0503.30

    Article  Google Scholar 

  58. Vinodhini R, Narayanan M (2009) Heavy metal induced histopathological alterations in selected organs of the Cyprinus carpio L. (common carp). Int J Environ Res. https://doi.org/10.22059/ijer.2009.35

  59. Wang L, Qian X, Zhang Y, … Cao X (2020) Enhancing sketch-based image retrieval by CNN semantic re-ranking. IEEE Trans Cybern 50:3330–3342. https://doi.org/10.1109/TCYB.2019.2894498

    Article  Google Scholar 

  60. Xia S, Chen P, Zhang J, Li X, Wang B (2017) Utilization of rotation-invariant uniform LBP histogram distribution and statistics of connected regions in automatic image annotation based on multi-label learning. Neurocomputing 228:11–18. https://doi.org/10.1016/j.neucom.2016.09.087

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    Ritesh Maurya

  2. Amity Institute of Biotechnology, Amity University, Noida, U.P., India

    Arti Srivastava & Ashutosh Srivastava

  3. Amity Institute of Marine Science and Technology, Amity University, Noida, U.P., India

    Ashutosh Srivastava

  4. Chhatrapati Shahu Ji Maharaj University, Kanpur, U.P., India

    Vinay Kumar Pathak

  5. Centre of Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, U.P., India

    Malay Kishore Dutta

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  1. Ritesh Maurya
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  2. Arti Srivastava
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  3. Ashutosh Srivastava
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  4. Vinay Kumar Pathak
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  5. Malay Kishore Dutta
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Correspondence to Malay Kishore Dutta.

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In our knowledge, as per the Indian laws, working with food fish, does not require ethical clearance.

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Maurya, R., Srivastava, A., Srivastava, A. et al. Computer aided detection of mercury heavy metal intoxicated fish: an application of machine vision and artificial intelligence technique. Multimed Tools Appl 82, 20517–20536 (2023). https://doi.org/10.1007/s11042-023-14358-5

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